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https://github.com/rxwei/cuda-swift
Parallel Computing Library for Linux and macOS & NVIDIA CUDA Wrapper
https://github.com/rxwei/cuda-swift
cublas cuda gpu parallel swift
Last synced: about 2 months ago
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Parallel Computing Library for Linux and macOS & NVIDIA CUDA Wrapper
- Host: GitHub
- URL: https://github.com/rxwei/cuda-swift
- Owner: rxwei
- License: mit
- Created: 2016-10-13T23:53:36.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-03-27T21:11:07.000Z (over 7 years ago)
- Last Synced: 2024-04-04T18:32:01.523Z (6 months ago)
- Topics: cublas, cuda, gpu, parallel, swift
- Language: Swift
- Homepage:
- Size: 315 KB
- Stars: 80
- Watchers: 5
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# cuda-swift
This project provides a native Swift interface to CUDA with the following
modules:
- [x] CUDA Driver API `import CUDADriver`
- [x] CUDA Runtime API `import CUDARuntime`
- [x] NVRTC - CUDA Runtime Compiler `import NVRTC`
- [x] cuBLAS - CUDA Basic Linear Algebra Subprograms `import CuBLAS`
- [x] Warp - GPU Acceleration Library `import Warp` ([Thrust](https://github.com/thrust/thrust) counterpart)
Any machine with CUDA 7.0+ and a CUDA-capable GPU is supported. Xcode Playground
is supported as well. Please refer to [Usage](#Usage)
and [Components](#Components).
## Quick look
### Value types
CUDA Driver, Runtime, cuBLAS, and NVRTC (real-time compiler) are wrapped in
native Swift types. Warp provides higher level value types, `DeviceArray` and
`DeviceValue`, with copy-on-write semantics.
```swift
import Warp
/// Initialize two arrays on device
var x: DeviceArray = [1.0, 2.0, 3.0, 4.0, 5.0]
let y: DeviceArray = [1.0, 2.0, 3.0, 4.0, 5.0]
/// Scalar map operations
x.incrementElements(by: 2) // x => [2.0, 3.0, 4.0, 5.0, 6.0] on device
x.multiplyElements(by: 2) // x => [2.0, 4.0, 6.0, 8.0, 10.0] on device
/// Addition
x.formElementwise(.addition, with: y) // x => [3.0, 6.0, 9.0, 12.0, 15.0] on device
/// Dot product
x • y // => 165.0
/// Sum
x.sum() // => 15
/// Absolute sum
x.sumOfAbsoluteValues() // => 15
/// Transform by 1-place math functions
x.transform(by: .sin)
x.transform(by: .tanh)
x.transform(by: .ceil)
/// Elementwise operation
x.formElementwise(.addition, with: y)
x.formElementwise(.subtraction, with: y)
x.formElementwise(.multiplication, with: y)
x.formElementwise(.division, with: y)
/// Fill with the same value
var z = y
z.fill(with: 10.0)
/// Composite assignment
x.assign(from: .subtraction, left: y, multipliedBy: 100.0, right: z)
```
### Real-time compilation
#### Compile source string to PTX
```swift
import NVRTC
import CUDADriver
import Warp
let source: String =
+ "extern \"C\" __global__ void saxpy(float a, float *x, float *y, float *out, int n) {"
+ " size_t tid = blockIdx.x * blockDim.x + threadIdx.x;"
+ " if (tid < n) out[tid] = a * x[tid] + y[tid];"
+ "}";
let ptx = try Compiler.compile(source)
```
#### JIT-compile and load PTX using Driver API within a device context
```swift
try Device.main.withContext { context in
let module = try Module(ptx: ptx)
let function = module.function(named: "saxpy")!
let x: DeviceArray = [1, 2, 3, 4, 5, 6, 7, 8]
let y: DeviceArray = [2, 3, 4, 5, 6, 7, 8, 9]
var result = DeviceArray(capacity: 8)
try function<<<(1, 8)>>>[.float(1.0), .constPointer(to: x), .constPointer(to: y), .pointer(to: &result), .int(8)]
/// result => [3, 5, 7, 9, 11, 13, 15, 17] on device
}
```
## Package Information
Add a dependency:
```swift
.Package(url: "https://github.com/rxwei/cuda-swift", majorVersion: 1)
```
You may use the `Makefile` in this repository for you own project. No extra path
configuration is needed.
Otherwise, specify the path to your CUDA headers and library at `swift build`.
#### macOS
```
swift build -Xcc -I/usr/local/cuda/include -Xlinker -L/usr/local/cuda/lib
```
#### Linux
```
swift build -Xcc -I/usr/local/cuda/include -Xlinker -L/usr/local/cuda/lib64
```
## Components
### Core
- [x] CUDADriver - CUDA Driver API
- [x] `Context`
- [x] `Device`
- [x] `Function`
- [x] `PTX`
- [x] `Module`
- [x] `Stream`
- [x] `Unsafe(Mutable)DevicePointer`
- [x] `DriverError` (all error codes from CUDA C API)
- [x] CUDARuntime - CUDA Runtime API
- [x] `Unsafe(Mutable)DevicePointer`
- [x] `Device`
- [x] `Stream`
- [x] `RuntimeError` (all error codes from CUDA C API)
- [x] NVRTC - CUDA Runtime Compiler
- [x] `Compiler`
- [x] CuBLAS - GPU Basic Linear Algebra Subprograms (in-progress)
- [x] Level 1 BLAS operations
- [x] Level 2 BLAS operations (GEMV)
- [x] Level 3 BLAS operations (GEMM)
- [x] Warp - GPU Acceleration Library ([Thrust](https://github.com/thrust/thrust) counterpart)
- [x] `DeviceArray` (generic array in device memory)
- [x] `DeviceValue` (generic value in device memory)
- [x] Acclerated vector operations
- [x] Type-safe kernel argument helpers
### Optional
- [x] Swift Playground
- CUDADriver works in the playground. But other modules cause the "couldn't lookup
symbols" problem for which we don't have a solution until Xcode is fixed.
- To use the playground, open the Xcode workspace file, and add a library for
every modulemap under `Frameworks`.
## Dependencies
- [CCUDA (CUDA C System Module)](https://github.com/rxwei/CCUDA)
## License
MIT License
CUDA is a registered trademark of NVIDIA Corporation.